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1.
社会力量具备相应的经济基础和技术条件,对于公立互联网医院体系建设具有积极的促进作用。但是实践中仍然存在商业模式不完善、监管制度不健全等问题。基于经济学契约理论要义,提出在坚持激励与约束机制并举、平衡公私益关系的前提下,通过完善医保政策、构建互联网医疗服务价格分类管理机制来促进社会力量向医疗服务公益性目标回归,并通过构建完善的监督体制来约束部分社会力量的盲目逐利性行为。 相似文献
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Comprehensive evidence regarding the treatment of non-anaemic iron deficiency in patients undergoing valvular heart surgery is lacking. This study aimed to investigate the association between non-anaemic iron deficiency and postoperative outcomes in these patients. We retrospectively analysed 321 patients of which 180 (56%) had iron deficiency (defined as serum ferritin < 100 ng.ml-1 or < 300 ng.ml-1 with transferrin saturation < 20%). While the iron-deficient group had lower pre-operative haemoglobin levels than the non-iron deficient group (median (IQR [range]) 134 (127–141 [120–172]) g.l-1, 143 (133–150 [120–179]) g.l-1, p = 0.001), there was no between-group difference in allogeneic red blood cell transfusion. Median (IQR [range]) days alive and out of hospital at postoperative day 90 was 1 day shorter in the iron-deficient group (80 (77–82 [9–85]) days vs. 81 (79–83 [0–85]) days, p = 0.026). In multivariable analysis, only cardiopulmonary bypass duration (p = 0.032) and intra-operative allogeneic red blood cell transfusion (p = 0.011) were significantly associated with reduced days alive and out of hospital at postoperative day 90. Iron deficiency did not exert any adverse influence on secondary outcomes except length of hospital stay. Our findings indicate that non-anaemic iron deficiency alone is not associated with adverse effects in patients undergoing valvular heart surgery when it does not translate into an increased risk of allogeneic transfusion. 相似文献
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目的探究组织气氛在护士吞咽障碍知识水平与护理行为之间的中介效应。方法2019年6-7月,便利抽样法选取15个省市33所医院及医疗机构神经科、康复科具有吞咽障碍护理经验的231名护士为研究对象采用脑卒中吞咽障碍知识水平问卷、脑卒中吞咽障碍护理行为问卷及团队创新气氛量表对其进行调查。结果护士吞咽障碍知识水平总分为(56.69±13.50)分、护理行为得分为(140.65土32.02)分、组织气氛得分为(157.63±26.56)分。组织气氛与吞咽障碍知识水平(r=0.49,P<0.01)、护理行为(r=0.50,P<0.01)均呈正相关。组织气氛在知识水平和护理行为之间起中介作用,中介效应占总效应的83.18%。结论组织气氛在护士吞咽障碍知识水平与护理行为之间起中介作用,临床管理者可在提升护士吞咽障碍知识水平的基础上,通过提高组织气氛水平推进其护理行为。 相似文献
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《Diagnostic and interventional imaging》2020,101(9):555-564
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
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